Classification of Heart Sound Signals Based on AR Model

Rui, Hebing; Zhang, Henggui; Wang, Kuanquan; Li, Qince; Sheng, Zhiqiang; Zhao, Na

doi:10.22489/cinc.2016.177-133

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The purpose of using the HSMM model in this study was the estimation of the probability density function of the expected remaining time in each state. He and Zhang (2016) segmented PCG data based on wavelet decomposition and normalized Shannon energy. Pratima and Emin extracted feature vectors by dividing each cardiac cycle into 35 frames where each frame had a length of 150 ms and slide size of 25 ms (Upretee and Mehmet Emin 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the fact that the SVM (Bouril and Aleinikava 2016) and LSTM (Christopher Schölzel et al 2016) have shown good classification accuracies of 78.64% and 74.9%, respectively, researchers have carried out relevant studies by utilizing different features and classification techniques to achieve better accuracy (Homsi and Medina 2016, Grzegorczyk et al 2016, Ibarra-Hernández and Bertin 2018. Runnan classified the PCG signal by using an autoregressive (AR) model with an accuracy of 74% (He and Zhang 2016). In Mandal et al (2014) ICA was utilized and ten independent components were extracted.…”

Section: Introductionmentioning

confidence: 99%

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Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Khan

Abid²,

Khan³

2020

Physiol. Meas.

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Objective: Heart abnormality detection using heart sound signals (phonocardiogram (PCG)) has been an active research area for the last few decades. In this paper, automatic heart sound classification using segmented and unsegmented PCG signals is presented. Approach: In this paper: (i) we perform an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance; (ii) both segmented and unsegmented PCG signals are studied and important results concerning the respective feature subsets and their classification performances are reported; and (iii) different classification algorithms, including the support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory network (LSTMs), are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. Main results: It is observed that LSTM performs better on mel-frequency cepstral coefficient (MFCC) features extracted from unsegmented PCG data, with an area under curve (AUC) score of 91.39%, however, the MFCC features do not show a consistent performance with other classifiers (the second highest AUC score is 62.08% with the decision tree classifier). In contrast, in the case of time-frequency features from segmented data, the performance of all the classifiers is appreciable with AUC scores over 70%. In particular, the conventional machine learning techniques shows consistency in achieving over 80% in AUC scores. Significance: The results of this study highlight the importance of time and frequency domain features. Thus it is necessary to employ both the time and frequency features of segmented PCG signals to achieve improved classification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Khan

Abid²,

Khan³

2020

Physiol. Meas.

View full text Add to dashboard Cite

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“…5 Maryam et al 6 made classification of Heart sound signal using feature extraction techniques of curve fitting and Mel frequency cepstrum coefficients (MFCC) but absence of localization feature it performed misclassification of heart sound signals S 1 and S 2 . Another approach was made for classification of heart sound by He et al, 7 based on AR Modeling it also leads towards misclassification of signal due to less numbers of appropriate features. Further, efforts were made by Barma et al 8 for detection of third heart sound also, using non-linear signal decomposition and time–frequency localization but it fails in term of computational cost.…”

Section: Introductionmentioning

confidence: 99%

Detection and localization of S₁ and S₂ heart sounds by 3rd order normalized average Shannon energy envelope algorithm

Nath

Srivastava

Kulshrestha

et al. 2021

Proc Inst Mech Eng H

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Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

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Classification of Normal Heart Beats Using Spectral and Nonspectral Features for Phonocardiography Signals

Malik

Siddiqi

2018

Applications of Intelligent Technologies in Healthcare

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Classification of Heart Sound Signals Based on AR Model

Abstract: Heart sounds reflect information of the mechanical contraction of the heart in both of the Specificity (Sp) and overall score are respectively 0.87, 0.61, and 0.74.

Cited by 7 publications

References 12 publications

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Detection and localization of S₁ and S₂ heart sounds by 3rd order normalized average Shannon energy envelope algorithm

Classification of Normal Heart Beats Using Spectral and Nonspectral Features for Phonocardiography Signals

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Classification of Heart Sound Signals Based on AR Model

Abstract: Heart sounds reflect information of the mechanical contraction of the heart in both of the Specificity (Sp) and overall score are respectively 0.87, 0.61, and 0.74.

Cited by 7 publications

References 12 publications

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm

Classification of Normal Heart Beats Using Spectral and Nonspectral Features for Phonocardiography Signals

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Detection and localization of S₁ and S₂ heart sounds by 3rd order normalized average Shannon energy envelope algorithm